OpenRaiser/Intern-Atlas huggingface dataset:
Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network
Venue: arXiv cs.CR 2019
Year: 2019

With the globalization of the semiconductor manufacturing process, electronic devices are powerless against malicious modification of hardware in the supply chain. The ever-increasing threat of hardware Trojan attacks against integrated circuits has spurred a need for accurate and efficient detection methods. Ring oscillator network (RON) is used to detect the Trojan by capturing the difference in power consumption; the power consumption of a Trojan-free circuit is different from the Trojan-inserted circuit. However, the process variation and measurement noise are the major obstacles to detect hardware Trojan with high accuracy. In this paper, we quantitatively compare four supervised machine learning algorithms and classifier optimization strategies for maximizing accuracy and minimizing the false positive rate (FPR). These supervised learning techniques show an improved false positive rate compared to principal component analysis (PCA) and convex hull classification by nearly 40% while maintaining > 90\% binary classification accuracy.
A Survey on Data Plane Flexibility and Programmability in Software-Defined Networking
Venue: arXiv cs.NI 2019
Year: 2019

Software-defined networking (SDN) attracts the attention of the research community in recent years, as evidenced by a large number of survey and review papers. The architecture of SDN clearly recognizes three planes: application, control, and data plane. The application plane executes network applications; control plane regulates the rules for the entire network based on the requests generated by network applications; and based on the set rules, the controller configures the switches in the data plane. The role of the switch in the data plane is to simply forward packets based on the instructions given by the controller. By analyzing SDN-related research papers, it is observed that research, from the very beginning, is insufficiently focused on the data plane. Therefore, this paper gives a comprehensive overview of the data plane survey with particular emphasis on the problem of programmability and flexibility. The first part of the survey is dedicated to the evaluation of actual data plane architectures through several definitions and aspects of data plane flexibility and programmability. Then, an overview of SDN-related research was presented with the aim of identifying key factors influencing the gradual deviation from the original data plane architectures given with ForCES and OpenFlow specifications, which we called the data plane evolution. By establishing a correlation between the treated problem and the problem-solving approaches, the limitations of ForCES and OpenFlow data plane architectures were identified. Based on identified limitations, a generalization of approaches to addressing the problem of data plane flexibility and programmability is made. By examining generalized approaches, open issues have been identified, establishing the grounds for future research directions proposal.
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
Venue: arXiv cs.LG 2019
Year: 2019

Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware classification, where expert skills are necessary to identify useful features. Recent work, however, has shown that deep learning models can be used to automatically learn feature representations directly from the raw, unstructured bytes of the binaries themselves. In this paper, we explore what these models are learning about malware. To do so, we examine the learned features at multiple levels of resolution, from individual byte embeddings to end-to-end analysis of the model. At each step, we connect these byte-oriented activations to their original semantics through parsing and disassembly of the binary to arrive at human-understandable features. Through our results, we identify several interesting features learned by the model and their connection to manually-derived features typically used by traditional machine learning models. Additionally, we explore the impact of training data volume and regularization on the quality of the learned features and the efficacy of the classifiers, revealing the somewhat paradoxical insight that better generalization does not necessarily result in better performance for byte-based malware classifiers.
A Novel Blockchain-based Trust Model for Cloud Identity Management
Venue: arXiv cs.CR 2019
Year: 2019

Secure and reliable management of identities has become one of the greatest challenges facing cloud computing today, mainly due to the huge number of new cloud-based applications generated by this model, which means more user accounts, passwords, and personal information to provision, monitor, and secure. Currently, identity federation is the most useful solution to overcome the aforementioned issues and simplify the user experience by allowing efficient authentication mechanisms and use of identity information from data distributed across multiple domains. However, this approach creates considerable complexity in managing trust relationships for both the cloud service providers and their clients. Poor management of trust in federated identity management systems brings with it many security, privacy and interoperability issues, which contributes to the reluctance of organizations to move their critical identity data to the cloud. In this paper, we aim to address these issues by introducing a novel trust and identity management model based on the Blockchain for cloud identity management with security and privacy improvements.
Are cracked applications really free? An empirical analysis on Android devices
Venue: arXiv cs.CR 2019
Year: 2019

Android is among the popular platforms running on millions of smart devices, like smartphones and tablets, whose widespread adoption is seen as an opportunity for spreading malware. Adding malicious payloads to cracked applications, often popular ones, downloaded from untrusted third markets is a prevalent way for achieving the aforementioned goal. In this paper, we compare 25 applications from the official and third-party application stores delivering cracked applications. The behavioral analysis of applications is carried out on three real devices equipped with different Android versions by using five indicators: requested permissions, CPU usage, RAM usage and the number of opened ports for TCP and HTTP. Based on these indicators, we compute an application intention score and classify cracked applications as malicious or benign. The experimental results show that cracked applications utilize on average more resources and request access to more (dangerous) permissions than their official counterparts.
Introduction to Decentralization and Smart Contracts
Venue: arXiv cs.CR 2019
Year: 2019

The aim of this work is to study the use of decentralization and smart contracts on blockchain networks. We investigate the implementation and use of smart contracts on the platforms Bitcoin, Ethereum and Hyperledger Fabric. Additionally, we have researched consensus algorithms and their respective uses, mentioning both advantages and disadvantages where necessary. To conclude, there is an example contract that is meant to be a close to direct translation of a generic legal house rental contract to show how a legal contract can be translated.
Image Classification base on PCA of Multi-view Deep Representation
Venue: arXiv cs.CV 2019
Year: 2019

In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classification algorithms are mostly based on RGB images or grayscale images, and fail to make good use of the depth information about objects or scenes. The depth information in the images has a strong complementary effect, which can enhance the classification accuracy significantly. In this paper, we propose an image classification technology using principal component analysis based on multi-view depth characters. In detail, firstly, the depth image of the original image is estimated; secondly, depth characters are extracted from the RGB views and the depth view separately, and then the reducing dimension operation through the PCA is implemented. Eventually, the SVM is applied to image classification. The experimental results show that the method has good performance.
Unsupervised motion saliency map estimation based on optical flow inpainting
Venue: arXiv cs.CV 2019
Year: 2019

The paper addresses the problem of motion saliency in videos, that is, identifying regions that undergo motion departing from its context. We propose a new unsupervised paradigm to compute motion saliency maps. The key ingredient is the flow inpainting stage. Candidate regions are determined from the optical flow boundaries. The residual flow in these regions is given by the difference between the optical flow and the flow inpainted from the surrounding areas. It provides the cue for motion saliency. The method is flexible and general by relying on motion information only. Experimental results on the DAVIS 2016 benchmark demonstrate that the method compares favourably with state-of-the-art video saliency methods.
Towards Software Analytics: Modeling Maintenance Activities
Venue: arXiv cs.SE 2019
Year: 2019

Lehman's Laws teach us that a software system will become progressively less satisfying to its users over time, unless it is continually adapted to meet new needs. Understanding software maintenance can potentially relieve many of the pains currently experienced by practitioners in the industry and assist in reducing uncertainty, improving cost-effectiveness, reliability and more. The research community classifies software maintenance into 3 main activities: Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. In this work we seek to model software maintenance activities and design a commit classification method capable of yielding a high quality classification model. We performed a comparative analysis of our method and existing techniques based on 11 popular open source projects from which we had manually classified 1151 commits, over 100 commits from each of the studied projects. The model we devised was able to achieve an accuracy of 76% and Kappa of 63% (considered '"Good" in this context) for the test dataset, an improvement of over 20 percentage points, and a relative improvement of ~40% in the context of cross-project classification. We then leverage our commit classification method to demonstrate two applications: (1) a tool aimed at providing an intuitive visualization of software maintenance activities over time, and (2) an in-depth analysis of the relationship between maintenance activities and unit tests.
A Capsule-unified Framework of Deep Neural Networks for Graphical Programming
Venue: arXiv cs.LG 2019
Year: 2019

Recently, the growth of deep learning has produced a large number of deep neural networks. How to describe these networks unifiedly is becoming an important issue. We first formalize neural networks in a mathematical definition, give their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, using the concept of capsule to extend neural networks, we set up a capsule-unified framework for deep learning, including a mathematical definition of capsules, an induced model for capsule networks and a universal backpropagation algorithm for training them. Finally, we discuss potential applications of the framework to graphical programming with standard graphical symbols of capsules, neurons, and connections.
Induction, Coinduction, and Fixed Points: Intuitions and Tutorial
Venue: arXiv cs.LO 2019
Year: 2019

Recently we presented a concise survey of the formulation of the induction and coinduction principles, and some concepts related to them, in five different fields mathematical fields, hence shedding some light on the precise relation between these fields. In this article we present few tutorial examples---from set theory, number theory and real analysis---that illustrate these concepts, and the intuitions behind them, more concretely.
SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps
Venue: arXiv cs.CR 2019
Year: 2019

The evolution of drone technology in the past nine years since the first commercial drone was introduced at CES 2010 has caused many individuals and businesses to adopt drones for various purposes. We are currently living in an era in which drones are being used for pizza delivery, the shipment of goods, and filming, and they are likely to provide an alternative for transportation in the near future. However, drones also pose a significant challenge in terms of security and privacy within society (for both individuals and organizations), and many drone related incidents are reported on a daily basis. These incidents have called attention to the need to detect and disable drones used for malicious purposes and opened up a new area of research and development for academia and industry, with a market that is expected to reach $1.85 billion by 2024. While some of the knowledge used to detect UAVs has been adopted for drone detection, new methods have been suggested by industry and academia alike to deal with the challenges associated with detecting the very small and fast flying objects. In this paper, we describe new societal threats to security and privacy created by drones, and present academic and industrial methods used to detect and disable drones. We review methods targeted at areas that restrict drone flights and analyze their effectiveness with regard to various factors (e.g., weather, birds, ambient light, etc.). We present the challenges arising in areas that allow drone flights, introduce the methods that exist for dealing with these challenges, and discuss the scientific gaps that exist in this area. Finally, we review methods used to disable drones, analyze their effectiveness, and present their expected results. Finally, we suggest future research directions.
An extended polygonal finite element method for large deformation fracture analysis
Venue: arXiv cs.NA 2019
Year: 2019

The modeling of large deformation fracture mechanics has been a challenging problem regarding the accuracy of numerical methods and their ability to deal with considerable changes in deformations of meshes where having the presence of cracks. This paper further investigates the extended finite element method (XFEM) for the simulation of large strain fracture for hyper-elastic materials, in particular rubber ones. A crucial idea is to use a polygonal mesh to represent space of the present numerical technique in advance, and then a local refinement of structured meshes at the vicinity of the discontinuities is additionally established. Due to differences in the size and type of elements at the boundaries of those two regions, hanging nodes produced in the modified mesh are considered as normal nodes in an arbitrarily polygonal element. Conforming these special elements becomes straightforward by the flexible use of basis functions over polygonal elements. Results of this study are shown through several numerical examples to prove its efficiency and accuracy through comparison with former achievements.
A Review of Reinforcement Learning for Autonomous Building Energy Management
Venue: arXiv cs.LG 2019
Year: 2019

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined.
A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments
Venue: arXiv cs.GR 2019
Year: 2019

Interaction in virtual reality (VR) environments is essential to achieve a pleasant and immersive experience. Most of the currently existing VR applications, lack of robust object grasping and manipulation, which are the cornerstone of interactive systems. Therefore, we propose a realistic, flexible and robust grasping system that enables rich and real-time interactions in virtual environments. It is visually realistic because it is completely user-controlled, flexible because it can be used for different hand configurations, and robust because it allows the manipulation of objects regardless their geometry, i.e. hand is automatically fitted to the object shape. In order to validate our proposal, an exhaustive qualitative and quantitative performance analysis has been carried out. On the one hand, qualitative evaluation was used in the assessment of the abstract aspects such as: hand movement realism, interaction realism and motor control. On the other hand, for the quantitative evaluation a novel error metric has been proposed to visually analyze the performed grips. This metric is based on the computation of the distance from the finger phalanges to the nearest contact point on the object surface. These contact points can be used with different application purposes, mainly in the field of robotics. As a conclusion, system evaluation reports a similar performance between users with previous experience in virtual reality applications and inexperienced users, referring to a steep learning curve.
Cubic Planar Graphs that cannot be Drawn on few Lines
Venue: arXiv cs.CG 2019
Year: 2019

For every integer $\ell$, we construct a cubic 3-vertex-connected planar bipartite graph $G$ with $O(\ell^3)$ vertices such that there is no planar straight-line drawing of $G$ whose vertices all lie on $\ell$ lines. This strengthens previous results on graphs that cannot be drawn on few lines, which constructed significantly larger maximal planar graphs. We also find apex-trees and cubic bipartite series-parallel graphs that cannot be drawn on a bounded number of lines.
AutoML @ NeurIPS 2018 challenge: Design and Results
Venue: arXiv cs.LG 2019
Year: 2019

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.
Online Budgeted Learning for Classifier Induction
Venue: arXiv cs.LG 2019
Year: 2019

In real-world machine learning applications, there is a cost associated with sampling of different features. Budgeted learning can be used to select which feature-values to acquire from each instance in a dataset, such that the best model is induced under a given constraint. However, this approach is not possible in the domain of online learning since one may not retroactively acquire feature-values from past instances. In online learning, the challenge is to find the optimum set of features to be acquired from each instance upon arrival from a data stream. In this paper we introduce the issue of online budgeted learning and describe a general framework for addressing this challenge. We propose two types of feature value acquisition policies based on the multi-armed bandit problem: random and adaptive. Adaptive policies perform online adjustments according to new information coming from a data stream, while random policies are not sensitive to the information that arrives from the data stream. Our comparative study on five real-world datasets indicates that adaptive policies outperform random policies for most budget limitations and datasets. Furthermore, we found that in some cases adaptive policies achieve near-optimal results.
Automated Construction of Metric Maps using a Stochastic Robotic Swarm Leveraging Received Signal Strength
Venue: arXiv cs.RO 2019
Year: 2019

In this work, we present a novel automated procedure for constructing a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots. The robots obtain this data during random exploration of the domain by combining onboard odometry information with noisy measurements of signals received from transmitters located outside the domain. This data is processed offline to compute a density function of the free space over a discretization of the domain. We use persistent homology techniques from topological data analysis to estimate a value for thresholding the density function, thereby segmenting the obstacle-occupied region in the unknown domain. Our approach is substantiated with theoretical results to prove its completeness and to analyze its time complexity. The effectiveness of the procedure is illustrated with numerical simulations conducted on six different domains, each with two signal transmitters.
Maximal State Complexity and Generalized de Bruijn Words
Venue: arXiv cs.FL 2019
Year: 2019

We compute the exact maximum state complexity for the language consisting of $m$ words of length $N$, and characterize languages achieving the maximum. We also consider a special case, namely languages $C(w)$ consisting of the conjugates of a single word $w$. The words for which the maximum state complexity of $C(w)$ is achieved turn out to be a natural generalization of de Bruijn words. We show that generalized de Bruijn words exist for each length and consider the number of them.
An Integrated P2P Framework for E-Learning
Venue: arXiv cs.DC 2019
Year: 2019

The paper is concerned with the design and development of a P2P Presentation System (P2P-PS) for live streaming of lectures coupled with a shareable whiteboard. Video streaming uses a data-driven modified mesh architecture which supports dynamic fanout. The whiteboard may be used in synchronization with an active video streaming session. It is shared by both the presenter and the participants in the audience for graphic illustrations during the lecture. P2P-PS provides an important feature called {\em ask doubt} that allows any participant to send a query (or a doubt) during an active presentation session. The query is disseminated to all other peers by preserving the causality between {\em ask doubt} and its resolution. A buffered approach is employed to boost the performance of the whiteboard. The simulations experiments were performed with over 1000 peer nodes on Emulab using 200 physical nodes. The results show that the proposed overlay eventually stabilizes even in the presence of a churning rate of up to 30\%. The maximum path length being just six hops. The estimated throughput is found to be close to our theoretical results.